How to load data from Dremio to Convex

Learn how to use Airbyte to synchronize your Dremio data into Convex within minutes.

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Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Airbyte connections are:
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All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Dremio connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Convex for your extracted Dremio data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Dremio to Convex in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
Try Airbyte CloudManual method (the hard way)

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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.

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Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

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Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

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Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

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Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

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Tech Lead at Symend

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How to Sync to Manually

Step 1: Understand Your Data Requirements

Begin by clearly defining which data sets you need to migrate from Dremio to Convex. This involves identifying the tables, views, or specific queries in Dremio that contain the necessary data. Ensure you understand the schema, data types, and any transformations that must occur during the migration process.

Utilize Dremio's export capabilities to extract the required data. You can run SQL queries in Dremio to retrieve data and use the web interface or Dremio's REST API to export the data to CSV or JSON format. If using the API, authenticate and use the appropriate endpoint to download the results of your query.

Once you have the exported data files, inspect them to ensure they meet the data format and quality required by Convex. This might involve cleaning the data, transforming it into a suitable structure, or splitting large files into smaller chunks if necessary for easier processing.

Before importing data into Convex, ensure your environment is ready. This involves setting up the necessary schemas or collections in Convex that will store the data. Use Convex"s schema definition language or tools to create the necessary data structures to accommodate the incoming data.

Write a script or program to read the prepared data files and insert them into Convex. This script can be written in a programming language that can interact with Convex's API or database drivers (e.g., JavaScript, Python). Make sure to handle data type conversions and errors gracefully during this process.

Run the data import script to transfer the data from the exported files into Convex. Monitor the process to ensure that all data is imported correctly. If working with large datasets, consider batching the data import to manage memory and performance efficiently.

After the data import is complete, verify the data integrity and accuracy in Convex. Compare sample records between Dremio and Convex to ensure the migration was successful. Run queries in Convex to validate that the data is correct, complete, and in the desired format. Make any necessary adjustments to the data or import process based on your findings.

By following these steps, you'll successfully move data from Dremio to Convex without relying on third-party connectors or integrations.